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Browsing by Author "Mohan, Pradeep"

Now showing 1 - 4 of 4
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    Cascading spatio-temporal pattern discovery
    (2011-05-02) Mohan, Pradeep; Shekhar, Shashi; Shine, James A.; Rogers, James P.
    Given a collection of Boolean spatio-temporal (ST) event-types, the cascading spatio-temporal pattern (CSTP) discovery process finds partially ordered subsets of these event-types whose instances are located together and occur serially. For example, analysis of crime datasets may reveal frequent occurrence of misdemeanors and drunk driving after and near bar closings on weekends, as well as after and near large gatherings such as football games. Discovering CSTPs from ST datasets is important for application domains such as public safety (e.g. identifying crime attractors and generators) and natural disaster planning(e.g. preparing for hurricanes). However, CSTP discovery presents multiple challenges; three important ones are (1) the exponential cardinality of candidate patterns with respect to the number of event types, (2) computationally complex ST neighborhood enumeration required to evaluate the interest measure and (3) the difficulty of balancing computational complexity and statistical interpretation. Current approaches for ST data mining focus on mining totally ordered sequences or unordered subsets. In contrast, our recent work explores partially ordered patterns. Recently, we represented CSTPs as directed acyclic graphs; proposed a new interest measure, the cascade participation index; outlined the general structure of a cascading spatio-temporal pattern miner (CSTPM); evaluated filtering strategies to enhance computational savings using a real world crime dataset and proposed a nested loop based CSTPM to address the challenge posed by exponential cardinality of candidate patterns. This paper adds to our recent work by offering a new computational insight, namely, that the computational bottleneck for CSTP discovery lies in the interest measure evaluation. With this insight, we propose a new CSTPM based on spatio-temporal partitioning that significantly lowers the cost of interest measure evaluation. Analytical evaluation shows that our new CSTPM is correct and complete. Results from significant amount of new experimental evaluation with both synthetic and real data show that our new ST partitioning based CSTPM outperforms the CSTPM from our previous work. We also present a case study that verifies the applicability of CSTP discovery process.
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    Cascading Spatio-temporal pattern discovery: A summary of results
    (2010-01-14) Mohan, Pradeep; Shekhar, Shashi; Shine, James A.; Rogers, James P.
    Given a collection of Boolean spatio-temporal(ST) event types, the cascading spatio-temporal pattern (CSTP) discovery process finds partially ordered subsets of event-types whose instances are located together and occur in stages. For example, analysis of crime datasets may reveal frequent occurrence of misdemeanors and drunk driving after bar closings on weekends and after large gatherings such as football games. Discovering CSTPs from ST datasets is important for application domains such as public safety (e.g. crime attractors and generators) and natural disaster planning(e.g. hurricanes). However, CSTP discovery is challenging for several reasons, including both the lack of computationally efficient, statistically meaningful metrics to quantify interestingness, and the large cardinality of candidate pattern sets that are exponential in the number of event types. Existing literature for ST data mining focuses on mining totally ordered sequences or unordered subsets. In contrast, this paper models CSTPs as partially ordered subsets of Boolean ST event types. We propose a new CSTP interest measure (the Cascade Participation Index) that is computationally cheap (O(n2) vs. exponential, where n is the dataset size) as well as statistically meaningful. We propose a novel algorithm exploiting the ST nature of datasets and evaluate filtering strategies to quickly prune uninteresting candidates. We present a case study to find CSTPs from real crime reports and provide a statistical explanation. Experimental results indicate that the proposed multiresolution spatio-temporal(MST) filtering strategy leads to significant savings in computational costs.
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    Crime pattern analysis: A spatial frequent pattern mining approach
    (2012-05-10) Shekhar, Shashi; Mohan, Pradeep; Oliver, Dev; Zhou, Xun
    Crime pattern analysis (CPA) is the process of analytical reasoning facilitated by an understanding about the nature of an underlying spatial framework that generates crime. For example, law enforcement agencies may seek to identify regions of sudden increase in crime activity, namely, crime outbreaks. Many analytical tools facilitate this reasoning process by providing support for techniques such as hotspot analysis. However, in practice, police departments are desirous of scalable tools for existing techniques and new insights including, interaction between different crime types. Identifying new insights using scalable tools may help reduce the human effort that may be required in CPA. Formally, given a spatial crime dataset and other information familiar to law enforcement agencies, the CPA process identifies interesting, potentially useful and previously unknown crime patterns. For example, analysis of an urban crime dataset might reveal that downtown bars frequently lead to assaults just after bar closing. However, CPA is challenging due to: (a) the large size of crime datasets, and (b) a potentially large collection of interesting crime patterns. This chapter explores, spatial frequent pattern mining (SFPM), which is a spatial data driven approach for CPA and describes SFPM in the context of one type of CPA, outbreak detection. We present a case study to discover interesting, useful and non-trivial crime outbreaks in a dataset from Lincoln, NE. A review of emerging trends and new research needs in CPA methods for study to discover interesting, useful and non-trivial crime outbreaks in a dataset from outbreak detection is also presented.
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    Spatio-temporal frequent pattern mining for public safety:concepts and techniques.
    (2012-05) Mohan, Pradeep
    Spatio-temporal frequent pattern mining (SFPM) is the process of discovering interesting, useful and non-trivial patterns from large spatial or spatio-temporal datasets. For example, analysis of crime datasets may reveal frequent patterns such as partially ordered subsets of different crime types in the vicinity of bars. SFPM is important for societal applications such as public safety and can help decision makers design important strategies for mitigat- ing crime. In a typical public safety scenario, users provide several inputs including, a collection of crime reports, a suitable spatial or spatio-temporal neighborhood definition and inter- estingness thresholds. Given these inputs, SFPM discovers patterns that satisfy the inter- estingness criterion specified by the user. However, SFPM in the context of public safety presents multiple challenges. First, spatio-temporal temporal datasets possess unique prop- erties (e.g. partial order and heterogenity) that highlight the need for new and alternative pattern semantics. Second, most SFPM scenarios often require a delicate balance between finding statistically meaningful patterns and achieving computational scalability. Third, the number of plausible patterns in many spatio-temporal datasets may be exponential in the cardinality of the set of event types. Existing research in spatial and spatio-temporal data mining(STDM) is not designed to account for semantics of spatio-temporal datasets such as partial order and heterogenity. In addition, existing STDM techniques do not adequately balance the potentially conflicting requirements of computational scalability and statistical interpretation. In contrast, this thesis explores two novel pattern families that are designed to model unique semantics of spatio-temporal datasets, namely, cascading spatio-temporal patterns (CSTPs) and regional co-location patterns (RCPs). CSTPs and RCPs are specifically de- signed to address semantics of spatio-temporal data such as spatio-temporal partial order and spatial heterogenity respectively. This thesis explores the discovery of CSTPs and RCPs from large spatio-temporal crime datasets in detail and makes the following contributions: (a) We design new interest measures for these pattern families that are statistically inter- pretable and possess attractive computational properties for the design of computationally efficient algorithms; (b) We propose novel pattern mining algorithms for discovering a cor- rect and complet set of patterns; (c) We present performance evaluations of the proposed algorithms using experiments with real and synthetic datasets and provide algebraic cost models that analyze worst case costs of different computational approaches; and (d) We present case studies using these pattern families to validate the real world applicability of SFPM. Experimental results show that different SFPM algorithms which exploit special seman- tics of spatio-temporal datasets (e.g. positive autocorrelation) and special computational properties of interest measures enhance computational savings. Case studies demonstrate the real world applicability of proposed SFPM techniques via interesting, potentially useful and non-trivial patterns discovered from crime datasets.